YAP Signaling Regulates the Cellular Uptake and Therapeutic Effect of Nanoparticles.

Interactions between living cells and nanoparticles are extensively studied to enhance the delivery of therapeutics. Nanoparticles size, shape, stiffness, and surface charge are regarded as the main features able to control the fate of cell-nanoparticle interactions. However, the clinical translation of nanotherapies has so far been limited, and there is a need to better understand the biology of cell-nanoparticle interactions. This study investigates the role of cellular mechanosensitive components in cell-nanoparticle interactions. It is demonstrated that the genetic and pharmacologic inhibition of yes-associated protein (YAP), a key component of cancer cell mechanosensing apparatus and Hippo pathway effector, improves nanoparticle internalization in triple-negative breast cancer cells regardless of nanoparticle properties or substrate characteristics. This process occurs through YAP-dependent regulation of endocytic pathways, cell mechanics, and membrane organization. Hence, the study proposes targeting YAP may sensitize triple-negative breast cancer cells to chemotherapy and increase the selectivity of nanotherapy.

Fate of Gold Nanoparticles in Laser Desorption/Ionization Mass Spectrometry: Toward the Imaging of Individual Nanoparticles.

This study focuses on mapping the spatial distribution of Au nanoparticles (NPs) by laser desorption/ionization mass spectrometry imaging (LDI MSI). Laser interaction with NPs and associated phenomena, such as change of shape, melting, migration, and release of Au ions, are explored at the single particle level. Arrays of dried droplets containing low numbers of spatially segregated NPs were reproducibly prepared by automated drop-on-demand piezo-dispensing and analyzed by LDI MSI using an ultrahigh resolution orbital trapping instrument. To enhance the signal from NPs, an in source gas-phase chemical reaction of generated Au ions with xylene was employed. The developed technique allowed the detecting, chemical characterization, and mapping of the spatial distribution of Au NPs; the ion signals were detected from as low as ten 50 nm Au NPs on a pixel. Furthermore, the Au NP melting dynamics under laser irradiation was monitored by correlative atomic force microscopy (AFM) and scanning electron microscopy (SEM). AFM measurements of Au NPs before and after LDI MSI analysis revealed changes in NP shape from a sphere to a half-ellipsoid and total volume reduction of NPs down to 45% of their initial volume.

Scalable Production of Monodisperse Bioactive Spider Silk Nanowires.

Elongated protein-based micro- and nanostructures are of great interest for a wide range of biomedical applications, where they can serve as a backbone for surface functionalization and as vehicles for drug delivery. Current production methods for protein constructs lack precise control of either shape and dimensions or render structures fixed to substrates. This work demonstrates production of recombinant spider silk nanowires suspended in solution, starting with liquid bridge induced assembly (LBIA) on a substrate, followed by release using ultrasonication, and concentration by centrifugation. The significance of this method lies in that it provides i) reproducability (standard deviation of length <13% and of diameter <38%), ii) scalability of fabrication, iii) compatibility with autoclavation with retained shape and function, iv) retention of bioactivity, and v) easy functionalization both pre- and post-formation. This work demonstrates how altering the function and nanotopography of a surface by nanowire coating supports the attachment and growth of human mesenchymal stem cells (hMSCs). Cell compatibility is further studied through integration of nanowires during aggregate formation of hMSCs and the breast cancer cell line MCF7. The herein-presented industrial-compatible process enables silk nanowires for use as functionalizing agents in a variety of cell culture applications and medical research.

Correction of AFM data artifacts using a convolutional neural network trained with synthetically generated data.

AFM microscopy from its nature produces outputs with certain distortions, inaccuracies and errors given by its physical principle. These distortions are more or less well studied and documented. Based on the nature of the individual distortions, different reconstruction and compensation filters have been developed to post-process the scanned images. This article presents an approach based on machine learning - the involved convolutional neural network learns from pairs of distorted images and the ground truth image and then it is able to process pairs of images of interest and produce a filtered image with the artifacts removed or at least suppressed. What is important in our approach is that the neural network is trained purely on synthetic data generated by a simulator of the inputs, based on an analytical description of the physical phenomena causing the distortions. The generator produces training samples involving various combinations of the distortions. The resulting trained network seems to be able to autonomously recognize the distortions present in the testing image (no knowledge of the distortions or any other human knowledge is provided at the test time) and apply the appropriate corrections. The experimental results show that not only is the new approach better or at least on par with conventional post-processing methods, but more importantly, it does not require any operator's input and works completely autonomously. The source codes of the training set generator and of the convolutional neural net model are made public, as well as an evaluation dataset of real captured AFM images.